Spatial multiplexing is a transmission technique used in Multiple Input Multiple Output (MIMO)-based communication systems, where separately encoded signals (consider each signal sent from a constellation having M=2q symbol points) are transmitted over n transmit antennas. At the receiver side, multiple receive antennas m are used to receive the transmitted signal.
Current uniform set-partitioning based detectors in receivers used in communication systems have poor diversity and low Signal-to-Noise Ratio (SNR) gains at high complexity. At a receiver in a communication system, although optimum performance can be obtained using Maximum Likelihood Detector (MLD) providing a diversity gain m for each detected data stream, its major drawback is a high computational effort of O(Mn)=O(2qn) which grows exponentially with q and n. Hence a reduction in complexity is desired in practice.
For MIMO Spatial Multiplexing (SM) systems, Sphere Decoder (SD) provides MLD performance at low complexity. However, the major disadvantage of SD and its variants is their variable complexity depending on instantaneous SNR, leading to hardware implementation difficulties.
Further, a near-optimal, fixed-complexity detector called the V-BLAST coset detector (V-BLAST-CD) was shown to provide a near-ML performance at low-to-intermediate SNR values with two streams, with performance deterioration with an increase in SNR or the number of streams. Although a modification of V-BLAST-CD improved its performance, it involved the use of adaptive M-algorithm and variable set-partitions, thereby rendering it a variable-complexity receiver.
A “Fixed-complexity Sphere Decoder” (FSD) has been suggested to simultaneously tackle the problem of achieving near-ML performance in MIMO detection, while overcoming the variable complexity and sequential tree-search problems associated with the SD. The FSD involves two operations that are performed repeatedly such as channel reordering pre-processing operation followed by successive detection operation. A survivor vector is selected in the end to get the solution. The pre-processing technique greatly improves the performance of the FSD, while allowing for a reduction in complexity during successive detection operation. It was proved that FSD achieves the same diversity as MLD while yielding a quasi-ML performance at a very low and fixed complexity. One disadvantage that has been identified with the FSD is that its complexity increases for MIMO configurations larger than 4×4. One solution to minimize this increase in complexity is to discard some of the survivor paths during the later stages of successive detection. However, such a solution requires sorting across paths to choose the survivors.